Impact of the Henan Section of the Central Line Project of South-to-North Water Diversion on the High-Quality Development of Water-Receiving Cities

Abstract

The Henan section of the Central Line Project of South-to-North Water Diversion (CLPSNWD) plays a crucial role within the overall project, serving as both the water source area and the largest recipient of water. This study aims to construct a comprehensive evaluation index system for the high-quality development (HQD) of water-receiving cities (WRCs), considering both the “quantity” and “quality” aspects. Based on the dimensions of economic vitality, people’s livelihoods, environment, and green development, twelve indicators were assessed to examine the influence of the Henan section of the CLPSNWD on the HQD of WRCs. The analysis reveals the following findings: (1) The CLPSNWD has a more significant influence on the economic vitality and improvement of livelihoods in the cities along its route compared to its impact on environment and green development. (2) Among the cities along the route, Anyang, Hebi, and Zhengzhou experience a higher level of impact from the project compared to other cities along the route. (3) Analyzing the impact in different dimensions reveals that in the dimension of economic vitality, the impact of each city on HQD is similar to the overall trend. In the dimension of people’s livelihood and environment, Hebi has experienced the greatest amount of change, while in the dimension of green development, the cities are more unstable, with increases and decreases.

1. Introduction

The distribution of water resources in China exhibits the fundamental characteristic of the south being rich in water, while the north faces water scarcity, resulting in an imbalanced spatial distribution [1]. Henan Province, located in northern China and recognized as a significant agricultural province, experiences a per capita water resource availability of only 383 m³, which is merely one-fifth of the national average. As a result, it is categorized as an extremely water-deficient province and encounters persistent water supply insufficiency [2]. Since the completion of the Central Line Project of South-to-North Water Diversion (CLPSNWD) in late 2014, more than 50 billion m³ of water have been transferred, directly benefiting a population of 140 million. The project has played a crucial role in promoting coordinated development between the north and south of China and achieving a balanced allocation of water resources [3]. In the recipient area of the Henan section of the CLPSNWD, 83 water plants have been established to supply a total of 13.5 billion m³ of water to Henan Province. This significant water supply has effectively alleviated the shortage of water resources in the province and aided in overcoming water-related challenges [4], thereby providing a guarantee for its high-quality development (HQD).

In order to assess the impact of the Henan section of the CLPSNWD on the HQD of the water-receiving cities (WRCs) in an objective manner, this study takes into account both the “quantity” and “quality” aspects based on the three principles of HQD. An evaluation index system for the HQD of the Henan section of the CLPSNWD is constructed [5], with a specific focus on economic vitality, peoples livelihoods, environment, and green development [6]. To evaluate the HQD of WRCs in the Henan section of the CLPSNWD, the entropy weight-fuzzy comprehensive evaluation model is applied [7]. The objective is to provide a reference for the HQD of WRCs in the Henan section, as depicted in Figure 1.

As social development stages evolve, the research focus of scholars, both within China and internationally, has shifted from achieving mere “development” to achieving HQD. The origins and impacts of HQD have become crucial areas of investigation. Wheeler [8] analyzed the concept of a “growth disaster” in development, shifting the research focus from studying the sources and driving forces of social development growth to examining the consequences and quality of such growth. Barro [9] further explored the influence of various factors, including the environment, society, religion, and law, on the quality of development. In the research on the impact of HQD on society, Ji et al. [10] discovered that the importance lies in improving labor productivity and total factor productivity. Liu et al. [11] believed that HQD is the result of optimizing economic structure, transforming old and new drivers of growth, promoting coordinated economic and social development, and significantly improving people’s living standards after a certain stage of economic development. Yang [12] proposed that HQD refers to development that effectively meets the growing aspirations that people have for a better life. HQD is characterized by endogeneity, with green development becoming the universal form, openness becoming a necessary path, and sharing becoming a fundamental goal. Zhang et al. [13] argued that HQD is efficient, equitable, and environmentally sustainable, and has the goal of meeting the growing aspirations that people have for a better life. Gu et al. [14] found that high-quality development requires the leading role of scientific and technological innovation at a higher level and in a wider range. Jahanger [15] studied the impact of the quality of Foreign Direct Investment (FDI) on the HQD of inland provinces in China. Previous research has shown that the pursuit of gross domestic product (GDP) growth alone, focusing solely on the “quantity” rather than the “quality” of development, has, to some extent, promoted social development but has also brought with it negative effects such as environmental degradation and regional imbalances, hindering further social development. Therefore, emphasizing sustainable development that values the ecological environment and the relationship between humans and nature is key to achieving high-quality social development.

In recent years, numerous domestic scholars have conducted comprehensive research on the impacts of the CLPSNWD, including its effects on the HQD of the recipient areas. Zhao [16] conducted a study on Handan City and found that the Middle Route of the CLPSNWD effectively promoted the city’s sustainable development by analyzing aspects such as economic development and soil and water conservation. Jia [17] analyzed the impact of the CLPSNWD on the socio-economic development of the recipient areas and identified both positive and negative effects. This research revealed certain deficiencies in the evaluation of mega-projects like the CLPSNWD. Zhou et al. [18] analyzed the characteristics of land use evolution in the water source areas of the Middle Route and further researched regional ecological compensation standards based on the evaluation of ecosystem services in those areas. Han et al. [19] analyzed the fundamental impact of the water supply structure and pattern in the recipient areas of the CLPSNWD. They categorized the basic effects of HQD in the recipient areas into five aspects: social effects, economic effects, ecological effects, systemic effects, and induced effects. Liu et al. [20] summarized the principles of “water conservation as a priority, spatial balance, systematic governance, and dual efforts” for water management. They emphasized the fundamental support that water resources provide for the HQD of the recipient areas of the Middle Route, focusing on scientific approaches to water-related issues such as water resources, water ecology, and the water environment. It is evident from the mentioned studies that many scholars have conducted research considering four key aspects: economy, society, ecology, and environment. Analyzing HQD requires assessing the economic benefits, social benefits, ecological benefits, and environmental benefits. Therefore, when examining the impacts of the Middle Route of the CLPSNWD on HQD, it is crucial to consider research on economic vitality, people’s livelihoods, environment, and green development along the project’s route. This comprehensive approach allows for a more coherent and holistic understanding of the project’s impact on HQD.

Many domestic and international scholars have conducted comprehensive evaluation studies using various modeling methods and evaluation systems to assess the impacts of the CLPSNWD on HQD. Quigley et al. [21] utilized an ecological composite integrity evaluation system to assess the ecological security status of grassland, forest, and aquatic subsystems in the Columbia River Basin. Jin et al. [22] employed indicators such as the coefficient of variation, Herfindahl–Hirschman index, Theil entropy index, and average logarithmic deviation to calculate regional development differences in China. Ma et al. [23] constructed an economic growth quality index system and used a Vector Autoregression (VAR) model to analyze the impact of financial development on economic growth quality. Wang et al. [24] evaluated economic HQD disparities across different regions in China using per capita GDP and Defense Meteorological Satellite Program/Operational Linescan System nighttime light remote sensing data. Kattel et al. [25] emphasized the flexible adaptive water infrastructure framework established by the CLPSNWD and its role in enhancing personal and collective connectivity, improving resident’s lives, and addressing water resource shortages. Peng et al. [26] quantitatively assessed the socio-economic impacts of the project on Beijing using a computable general equilibrium model, comparing different water supply scenarios to provide insights into the rational allocation and management of local water resources. Du et al. [27] conducted an impact analysis on water balance and ground subsidence in Beijing, a recipient city of the CLPSNWD, using the Interferometric Synthetic Aperture Radar (InSAR) time series method. Yang et al. [28] utilized a combination weighting and trapezoidal cloud model comprehensive evaluation method to establish a benefit evaluation index system to evaluate the cost and efficiency of the East Route of the CLPSNWD, conducting a comprehensive evaluation of project benefits with the Jiangsu section as an example. Wang et al. [29] employed the entropy weight method to systematically explain the specific mechanism of the project’s role in HQD and examined the effects of science and technology finance on China’s HQD and its influencing factors. Zhang et al. [30] introduced the Structural Equation Model (SEM) to depict the comprehensive impact of FDI dynamically and comprehensively on HQD.

Based on the research of previous scholars, we found that there is limited research on the impacts of the CLPSNWD on the WRCs, and even fewer studies on the comprehensive effects of the project on the HQD of these cities in terms of economic, social, ecological, and environmental dimensions. By reviewing previous research papers on impact assessment and HQD, the following shortcomings have been identified:

(1)

One of the limitations of the previous studies of high-quality development is the fact that they mainly focus on the high-quality development of the economy, while the in the new era of high-quality development we must pay more attention to the ecological environment and the harmonious development of the relationship between human beings and nature on the basis of the economy, and to focus on the quality of high-quality development rather than just development itself.

(2)

Regarding impact assessment methods, both domestic and international scholars have employed comprehensive evaluation methods such as the Analytic Hierarchy Process (AHP), among others. However, these methods are subject to a certain degree of subjectivity and uncertainty. In the evaluation of the impacts of the CLPSNWD on the WRC, the assessments often focused on singular aspects such as economic development or ecological conservation, without providing a comprehensive evaluation of the impacts on these cities from various perspectives.

Building upon the existing research, this study selects eight WRCs in the Henan section of the CLPSNWD as the research objects. The entropy weight method and fuzzy evaluation method are used to establish an impact assessment model for HQD, aiming to explore the impacts of the CLPSNWD on the HQD of the WRCs from 2010 to 2019.

2. Materials and Methods

2.1. Data Resources

This article presents a statistical analysis of data from 2010 to 2019 for the WRCs in the Henan section of the Central Route of the Middle Route of the CLPSNWD, including Zhengzhou, Pingdingshan, Anyang, Hebi, Xinxiang, Jiaozuo, Xuchang, and Nanyang. The analysis focuses on four aspects: economic vitality, people’s livelihoods, the environment, and green development. The objective of this analysis is to provide data that will be used in the subsequent sections to construct a high-quality evaluation indicator system. The data utilized in this study were sourced from various reliable sources, including the annual statistical yearbooks of Henan Province, the statistical yearbooks of the prefecture-level cities, the annual statistical reports of Henan Province, and the historical water resources bulletins of Henan Province. In instances where minor data gaps existed for certain years, interpolation methods were employed to supplement and enhance the data, ensuring overall data integrity and coherence.

2.2. Principles of Evaluation Indicator System

HQD emphasizes a shift from extensive and inefficient growth patterns to intensive and sustainable growth patterns. This transformation aims to bring about fundamental changes in various aspects, including green development, coordinated development, innovative development, open development, and shared development. The ultimate goal is to fulfill people’s aspirations for a better life. To assess HQD effectively, it is crucial to develop an evaluation indicator system that reflects the essence of HQD and adheres to three fundamental principles. These principles should inherently reflect the four development concepts of HQD and their logical relationship [31].

(1)

Principle of Objectivity: The indicator system for HQD is a measure of people’s living standards and reflects the actual feelings of the people. Therefore, when selecting indicators, it is essential to adhere to the principle of objectivity, and the indicator data should objectively reflect the actual level.

(2)

Principle of Representativeness: When selecting indicators, not only should commonly use indicators that reflect HQD be considered, but representative indicators that are available and can comprehensively reflect the development status of the research area should also be selected based on the characteristics of the research region or industry.

(3)

Principle of Operability: The selected indicators must be operational to assess the degree of HQD in the research area. By evaluating the HQD index, the actual situation of HQD in the research area can be reflected, development gaps can be identified, existing problems can be addressed, and the gap between current life and a better life can be further narrowed.

2.3. Construction of Evaluation Indicator System

Based on the connotations of HQD, this study considers both the “quantity” aspect and the “quality” aspect in constructing the indicator system. The indicator system for HQD of WRCs is built based on four dimensions: economic vitality, people’s livelihoods, the environment, and green development.

(1)

Economic vitality

Indicators of economic vitality primarily encompass GDP per capita, the proportion of secondary industry added value to GDP, trade dependence, the growth rate of general public budget revenue, and the urban registered unemployment rate. Among these indicators, the proportion of secondary industry added value to GDP measures the extent of China’s economic transformation from industrialization towards a service-oriented economic structure, ultimately aiming to establish a high-quality modern economic system. Trade dependence refers to the percentage of total export trade volume in relation to GDP, which serves as a significant indicator of the influence of international markets on economic development.

(2)

People’s livelihoods

The people’s livelihoods are reflected through six indicators: urbanization rate, per capita education expenditure, the number of beds per thousand people, water supply volume, annual per capita water consumption, and comprehensive water consumption rates for urban and rural living environments. The indicators of per capita education expenditure and the number of beds per thousand people express the increasing demand for public goods and services such as education and healthcare as people’s living standards improve, reflecting the aspirations of the people for a better life. The three indicators related to water, namely water supply volume, annual per capita water consumption, and comprehensive water consumption rates for urban and rural living environments, compare the changes in water resources utilization before and after the implementation of the CLPSNWD.

(3)

Environment

The environment is assessed using three indicators: total industrial smoke dust emissions, total industrial solid waste emissions, and industrial wastewater generation. The variations in emissions of these “three waste” elements from industrial activities signify the trajectory of China’s ongoing technological revolution and industrial transformation. These indicators, particularly the emissions of different pollutants, provide an objective evaluation of the current environment in the region.

(4)

Green development

Green development encompasses four indicators: per capita water resources, greening coverage of built-up areas, annual water shortage per 10,000 CNY of GDP, and water consumption per unit of GDP. These indicators highlight the symbiotic relationship between humans and nature and the sustainable utilization of water resources in socio-economic development. They serve as representative indicators strongly associated with green living and HQD.

Based on the studies by Zhang et al. [32] and Sun et al. [33] and the characteristics of CLPSNWD, the constructed HQD indicator system includes 4 dimensions and 12 indicators: economic vitality, people’s livelihoods, the environment, and green development. The evaluation indicators are shown in

Table 1. Indicator system for HQD evaluation.

Dimensions	Evaluation Indicators	Nature
B1: Economic vitality	C1. GDP per capita	+
	C2. The ratio of the value added by the secondary sector to GDP	−
B2: People’s livelihoods	C3. Urbanization rate	+
	C4. Water supply level	+
	C5. Annual per capita water consumption	−
	C6. Comprehensive water uses rate of urban and rural environment	+
B3: Environment	C7. Total industrial soot emissions	−
	C8. Production of industrial solid waste	−
	C9. Production of industrial wastewater	−
B4: Green development	C10. Per capita water resources	+
	C11. Annual water shortage rate	−
	C12. Water consumption per 10,000 CNY GDP	−

Note: “+” indicates a positive indicator, while “−” indicates a negative indicator.

.

2.4. Determining the Weights of Evaluation Indicators

(1)

Data Processing

Data processing primarily involves indicator normalization. Indicator normalization aims to standardize all indicators in the same direction, converting both positively and negatively oriented indicators into positive indicators. The principle is to preserve the distribution pattern of the original indicators and reflect their true distribution status. In the HQD indicator system, all indicator values are objective data. Based on this, this study employs indicator normalization to process the data.

(2)

Determining Weights for Dimensions and Indicators

Various methods can be used to measure the weights of dimensions and indicators in the HQD framework, such as Principal Component Analysis (PCA), Entropy Weight Method, and AHP. PCA can reduce the number of indicators but may lose the economic interpretation of the original variables. The Entropy Weight Method can accurately and objectively reflect the real development situation but may overlook the importance of dimensions or indicators. AHP can hierarchically and systematically address complex problems but requires expert support and cannot generate innovative solutions for decision making.

Based on relevant literature and considering the positive and negative impacts of human activities, this study adopts the Entropy Weight Method to measure the weights of dimensions and indicators. The main steps are as follows:

First, suppose there are n evaluation objects, and each evaluation object has m evaluation indicators, we can then construct a judgment matrix T:

$$T={\left(t_{ij}\right)}_{nm},i=\mathrm{1,2},\cdots ,n;j=\mathrm{1,2},\cdots ,m$$

(1)

In the equation, $t_{ij}$ represents the measured value of the j-th evaluation indicator for the i-th evaluation object.

Second, the judgment matrix T is normalized to obtain the standardized matrix D. The calculation method for D can be divided into the following two cases:

When the higher and better the value of the evaluation indicator is

$$D_{ij}=\frac{u_{ij}-u_{min}}{u_{max}-u_{min}}$$

(2)

When the lower and better the value of the evaluation indicator is

$$D_{ij}=\frac{u_{max}-u_{ij}}{u_{max}-u_{min}}$$

(3)

In the equation, $u_{max}$, $u_{min}$ refer to the maximum and minimum values of the same evaluation indicator, respectively.

$$H_j=\left(-\frac{1}{\mathrm{l}\mathrm{n}n}\right)\sum f_{ij}\mathrm{l}\mathrm{n}{f}_{ij}$$

(4)

In the equation, $f_{ij}=b_{ij}/\sum _{I=1}^S{b}_{ij}$, when $f_{ij}=0$, $\mathrm{l}\mathrm{n}{f}_{ij}$ has no sense. When $f_{ij}=0$, $f_{ij}ln{f}_{ij}=0$.

Fourth, the entropy weight of each evaluation indicator is determined as follows:

$$W_i=\frac{1-H_j}{m-\sum _{j=1}^n{H}_j}$$

(5)

And satisfies $\sum _{j=1}^n{w}_j$ = 1.

2.5. Fuzzy Comprehensive Evaluation Model

The evaluation indicator set $U$ is:

$$U=\left\{u_1,u_2,\cdots ,u_n\right\}$$

(6)

n represents the number of evaluation indicators.

Referring to the previous Table 1, the evaluation indicators for WRCs along the CLPSNWD Central Route can be defined as follows:

$$\mathrm{A}=\left\{{\mathrm{B}}_1,{\mathrm{B}}_2,{\mathrm{B}}_3,{\mathrm{B}}_4\right\};$$

(7)

$${\mathrm{B}}_1=\left\{{\mathrm{C}}_1,{\mathrm{C}}_2,\right\};{\mathrm{B}}_2=\left\{{\mathrm{C}}_3,{\mathrm{C}}_4,{\mathrm{C}}_5,{\mathrm{C}}_6\right\};{\mathrm{B}}_3=\left\{{\mathrm{C}}_7,{\mathrm{C}}_8,{\mathrm{C}}_9\right\};{\mathrm{B}}_4=\left\{{\mathrm{C}}_{10},{\mathrm{C}}_{11},{\mathrm{C}}_{12}\right\}$$

(8)

To evaluate the various aspects of the impact on WRCs, it is necessary to establish a measurable indicator evaluation set for grading the evaluation indicators. The evaluation set V for comprehensive evaluation is:

$$\mathrm{V}=\left\{{\mathrm{v}}_1,{\mathrm{v}}_2,\cdots ,{\mathrm{v}}_{\mathrm{m}}\right\}$$

(9)

m represents the number of evaluation levels, and the specific level division is determined based on the actual situation. In this study, the evaluation set is determined based on a literature review and expert suggestions as follows:

$$\mathrm{V}=\left\{\mathrm{W}\mathrm{e}\mathrm{a}\mathrm{k},\mathrm{S}\mathrm{m}\mathrm{a}\mathrm{l}\mathrm{l},\mathrm{M}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e},\mathrm{L}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e},\mathrm{S}\mathrm{i}\mathrm{g}\mathrm{n}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{n}\mathrm{t}\right\}$$

(10)

2.6. Establishment of Membership Degree Fuzzy Matrix

This study first established the membership degrees of each indicator based on the membership degree function, then constructed the membership degree fuzzy matrix for the i-th primary indicator corresponding to the secondary indicators. The matrix $R_i$ is shown as follows:

$$R_i=\left[\begin{array}{ccc}{r_i}_{11}& \cdots & {r_i}_{1k}\\ ⋮& \ddots & ⋮\\ {r_i}_{j1}& \cdots & {r_i}_{jk}\end{array}\right]$$

(11)

where ${r_i}_{jk}$ represents the membership degree of the j-th secondary indicator included in the i-th primary indicator for the k-th evaluation level. Referring to the works of Chu [34] and others, the Ridge-shaped distribution membership function is adopted to establish the membership degree matrix. The membership degree function is defined as follows:

$$\mathrm{W}\mathrm{e}\mathrm{a}\mathrm{k}\left\{\begin{array}{cc}1& x\le a\\ \frac{1}{2}-\frac{1}{2}\sin \frac{\pi }{b-a} \left(x-\frac{a+b}{2}\right)& a<x\le b\\ 0& x>b\end{array}\right.$$

(12)

$$\mathrm{Small} \left\{\begin{array}{c}0 x\le 0.5a\\ \\ \frac{1}{2}+\frac{1}{2}\sin \frac{\pi }{a-0.5a} \left(x-\frac{a+0.5a}{2}\right) 0.5a<x\le a\\ 1 a<x\le b\\ \frac{1}{2}-\frac{1}{2}\sin \frac{\pi }{c-b} \left(x-\frac{b+c}{2}\right) b<x\le c\\ 0 x>c\end{array}\right.$$

(13)

$$\mathrm{Moderate} \left\{\begin{array}{c}0 x\le a\\ \\ \frac{1}{2}+\frac{1}{2}\sin \frac{\pi }{b-a} \left(x-\frac{a+b}{2}\right) a<x\le b\\ 1 b<x\le c\\ \frac{1}{2}-\frac{1}{2}\sin \frac{\pi }{0.5(d+c)-c} \left(x-\frac{0.5(d+c)+c}{2}\right) c<x\le 0.5(d+c)\\ 0 x>0.5(d+c)\end{array}\right.$$

(14)

$$\mathrm{Large} \left\{\begin{array}{c}0 x\le b\\ \\ \frac{1}{2}+\frac{1}{2}\sin \frac{\pi }{c-b} \left(x-\frac{b+c}{2}\right) b<x\le c\\ 1 c<x\le d\\ \frac{1}{2}-\frac{1}{2}\sin \frac{\pi }{(d+0.5(d-c\left)\right)-d} \left(x-\frac{(d+0.5(d-c\left)\right)+d}{2}\right) d<x\le (d+0.5(d-c\left)\right)\\ 0 x>d\end{array}\right.$$

(15)

$$\mathrm{Significant} \left\{\begin{array}{c}0 x\le c\\ \\ \frac{1}{2}+\frac{1}{2}\sin \frac{\pi }{d-c} \left(x-\frac{d+c}{2}\right) c<x\le d\\ 1 x>d\end{array}\right.$$

(16)

By utilizing the obtained weight vector $W_i \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{m}\mathrm{e}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{h}\mathrm{i}\mathrm{p} \mathrm{m}\mathrm{a}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{x} R_i$, the fuzzy matrix composite operation was performed to derive an evaluation model for assessing the impact of the CLPSNWD on the HQD of WRCs $E_i$:

$$E_i=W_i\times R_i$$

(17)

Based on the computational results, an evaluation analysis of the impact on the HQD of WRCs along the CLPSNWD can be conducted utilizing various aspects.

3. Results and Discussion

3.1. Evaluation Criteria for WRC

Based on the research on regional water resource evaluation by Li et al. [35], and combining it with the standards and the criteria in China, the following evaluation criteria were defined and the comprehensive weight of each evaluation index was obtained. Please refer to

Table 2. The impact evaluation criteria for WRCs.

Evaluation Criteria	I (Weak Impact)	II (Small Impact)	III (Moderate Impact)	IV (Large Impact)	V (Significant Impact)
GDP Per capita/CNY	<40,000	40,000~50,000	50,000~60,000	60,000~70,000	>70,000
The ratio of the value added by the secondary sector to GDP/%	<50	50~55	55~60	60~60	>65
Urbanization rate/%	<45	45~50	50~55	55~60	>60
Water supply level/a hundred million m3	<4	4~6	6~8	8~10	>10
Annual per capita water consumption/m3	<200	200~250	250~300	300~350	>350
Comprehensive water consumption rate of urban and rural living environment/%	<13	13~18	18~23	23~28	>28
Total industrial soot emissions/ton	<30,000	30,000~40,000	40,000~50,000	50,000~60,000	>60,000
Production of industrial solid waste/Ten thousand tons	<300	300~700	700~1100	1100~1500	>1500
Production of industrial wastewater/Ten thousand tons	<6000	6000~9000	9000~12,000	12,000~15,000	>15,000
Per capita water resources/m3	<150	150~200	200~250	250~300	>300
Annual water shortage rate/%	<15	15~20	20~25	25~30	>30
Water consumption per 10,000 CNY GDP/ton	<40	40~50	50~60	60~70	>70

for the evaluation criteria. The criteria for the above classification are based on the average level of the water supply in the recipient areas, the standard of urban residential water consumption, the Henan Province Environment Statistical Yearbook, and the Water Resources Bulletin.

3.2. Weights of Evaluation Indicators for WRC

After the evaluation indicator system had been established, the AHP was used to calculate the weights of the criteria and indicators at both the criterion level and the indicator level. Subsequently, the entropy weighting method was applied to optimize the obtained weights. After the optimized weights for the criterion level and the corresponding weights for each indicator were obtained, the weights of each indicator relative to the objective were analyzed and calculated, and rankings were assigned to the indicator weights. The specific numerical results of the weights are shown in Figure 2.

(1)

From the weights of each dimension, green development has the highest weight at 36.28%, indicating that it is a key driver in promoting HQD in the region. Secondly, the weight of improving people’s livelihoods is 32.11%, highlighting that improving people’s livelihoods is the foundation of HQD. This reflects China’s commitment to putting people first and promoting the well-being of the people, closely integrating HQD with meeting the needs of a better life for the people. The weight of economic vitality ranks third at 21.98%, indicating that there is an inseparable relationship between economic development and HQD, and highlighting the importance of economic vitality.

(2)

From the weights of each indicator, per capita water resources have the highest weight, accounting for 20.68%. This suggests the importance of per capita water resources in the process of regional HQD and serves as a key indicator influenced by the CLPSNWD. The weight of per capita GDP is also relatively high at 12.97%, indicating that per capita GDP is a tangible representation of economic vitality, which is closely related to HQD. Therefore, per capita GDP can have a significant impact on the evaluation of regional HQD. The weight of the comprehensive water usage rates for urban and rural living environments is 12.52%, serving as an important indicator of local people’s livelihoods. Analyzing the comprehensive water usage rates for urban and rural living environments can provide insights into the utilization of water resources in the region and offer valuable references for studying the factors influencing HQD.

To sum up, in the weighting results for the entire evaluation index, green development is the most important dimension in which the CLPSNWD affects the HQD of cities along the route, and per capita water resources is the most important indicator, indicating that the impact of the CLPSNWD on cities mainly comes from the transportation of water resources, which radiates out to the cities along the route and provides sufficient water resources for their HQD.

3.3. Analysis of the Impact on WRC

After the comprehensive weights of each evaluation indicator were obtained, the evaluation criteria in Table 2 were used. The indicator data for each year from 2010 to 2019 were input into the ridge-shaped distribution membership function. Subsequently, the membership matrix and fuzzy comprehensive evaluation results for the WRCs in the Henan section of the CLPSNWD were calculated separately for the pre-water supply period (2010–2014) and the post-water supply period (2015–2019), as shown in Figure 3.

Comparing the bars in Figure 3, it can be observed that during the period of 2010–2019, the impact of the CLPSNWD on the HQD of WRCs in the Henan section shows an overall trend of transitioning from lower to higher levels in terms of economic vitality, people’s livelihoods, environment, and green development. Among them, Anyang, Hebi, and Zhengzhou experienced a faster growth in their impact levels, indicating that these areas have relatively scarce water resources and are therefore more affected by the implementation of the CLPSNWD. Nanyang, on the other hand, exhibited a slower growth in its impact level. This is because Nanyang serves as a water source area for the CLPSNWD and has abundant water reserves, resulting in a lesser impact after the project’s implementation.

Since the commissioning of the CLPSNWD in 2014, there have been varying degrees of impact on economic vitality, people’s livelihoods, the environment, and green development of WRC. By analyzing and calculating the extent of the impact on each dimension, a comprehensive evaluation of the project’s influence on the HQD of WRC can be obtained, as shown in Figure 4.

By comparing the impact of the CLPSNWD on the four dimensions of the HQD of cities within the Henan section from 2010 to 2014 and from 2015 to 2019, the following conclusions can be drawn. As a whole, the influence of the project on the urban areas affected by the water supply diversion is increasing, but some cities are less affected in certain dimensions and the cities within the project area are affected differently regarding each dimension. In the dimension of economic vitality, the degree of influence on the cities except Zhengzhou before and after the water supply has increased to different degrees. In the dimension of the improvement of people’s livelihood, it was found that Hebi, Pingdingshan and Nanyang experienced the greatest change in the degree of influence after the water supply diversion was implemented, among which Hebi has been the most affected. In the dimension of environmental conditions, Anyang, Hebi, and Zhengzhou have the most change in the degree of impact before and after the water diversion, and Hebi is still the most affected. In terms of green development, the impact of the project on the cities within the Henan section is unstable, with Anyang, Zhengzhou, and Pingdingshan being less affected and Hebi and Xuchang being more affected. In addition, through the overall analysis of the impact of the CLPSNWD on the HQD of the water-receiving cities in four dimensions, Xinxiang is the least affected by the water diversion project, and has experienced only a small change in the economic vitality dimension. The most affected city is Hebi, which has experienced changes in all dimensions, and the greatest changes in the dimensions of people’s livelihood improvement and environmental conditions.

4. Conclusions

4.1. Conclusions

This study utilized the entropy-weighted fuzzy comprehensive evaluation model to assess the impact of the CLPSNWD in Henan Province on the HQD of WRCs during 2010–2019 in terms of economic vitality, people’s livelihoods, the environment, and green development. The following conclusions can be drawn.

(1)

The CLPSNWD had a greater influence on the HQD of Anyang, Hebi, and Zhengzhou compared to other WRCs. These cities experienced higher levels of impact in terms of economic vitality, people’s livelihoods, the environment, and green development. Among the WRCs, Nanyang experienced the weakest impact in all four dimensions, with the smallest overall increase.

(2)

From the perspective of the impact of each dimension on the HQD of WRCs, since the CLPSNWD has been opened, the degree of impact on water-affected cities has generally increased in all dimensions, and only a few cities have experienced a reduction in these dimensions. In the dimension of economic vitality, the impact of each city on HQD is similar to the overall trend. In the dimension of people’s livelihood and the environment, Hebi has experienced the greatest change, while in the dimension of green development, the cities are more unstable, with increases and decreases. This indicates that the impacts of the CLPSNWD were not unified and sustainable.

(3)

The cities receiving water within the Henan section are more affected by the CLPSNWD in terms of economic vitality and people’s livelihood improvement than in terms of environmental conditions and green development. This indicates that the opening of the project has had a greater impact on the economic vitality of the cities and the improvement of people’s livelihoods dimensions of HQD. In the dimensions of environmental conditions and green development, the CLPSNWD has had less of an impact on the HQD of the recipient cities along the route.

4.2. Recommendations

Based on the above conclusions, this study proposes the following recommendations.

(1)

WRCs within the CLPSNWD area should consider establishing a more comprehensive evaluation system for HQD. These cities should aim for HQD in terms of economic vitality, people’s livelihoods, the environment, and green development, taking into account the overall development across these dimensions.

(2)

While utilizing the “South Water” resources in WRCs, appropriate resource compensation should be provided to the water source areas of the CLPSNWD. It is essential to protect the ecological environment of the water source areas and avoid excessive water diversions that could negatively impact local ecosystems. By continuously improving the compensation mechanism, the water source areas can actively contribute to freshwater resources without compromising their own HQD, thereby addressing water shortage issues in various regions.

(3)

WRCs within the CLPSNWD area should seize the opportunity to make rational use of the “South Water” resources. Each city should leverage its own advantages and establish a platform for common development. Promoting mutual exchange among the WRC and learning from the integrated development models of the “Yangtze River Delta” and the “Pearl River Delta” regions will be crucial. The goal is to develop economically distinctive zones that truly achieve comprehensive HQD for the WRCs within the CLPSNWD area.